Alarm fatigue management systems and methods

ABSTRACT

An apparatus for modifying alarms at a medical device for alarm fatigue management is provided and includes an alarm monitor, an alarm filter, an alarm modifier, a memory element for storing data, and a processor that executes instructions associated with the data, wherein the processor and the memory element cooperate such that the apparatus is configured for receiving an alarm condition from an alarm management engine, the alarm condition based on an alarm fatigue level of a user of the medical device, the alarm fatigue level based on at least a user fatigue model, a medical device model and a patient condition, receiving an alarm from the medical device, modifying the alarm according to the alarm condition, the alarm condition being configured to increase a likelihood of the user responding to the modified alarm, and generating an alarm indicator based on the modification.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/735,476, filed on Jan. 6, 2020, entitled ALARM FATIGUE MANAGEMENTSYSTEMS AND METHODS, which is a continuation of U.S. patent applicationSer. No. 16/155,006, filed on Oct. 9, 2018, now issued as U.S. Pat. No.10,524,712 on Jan. 7, 2020, and entitled ALARM FATIGUE MANAGEMENTSYSTEMS AND METHODS, which is a continuation of U.S. patent applicationSer. No. 14/738,658, filed on Jun. 12, 2015, now issued as U.S. Pat. No.10,123,729 on Nov. 13, 2018, and entitled ALARM FATIGUE MANAGEMENTSYSTEMS AND METHODS which relates to and claims the benefit of priorityunder 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No.62/011,643, filed on Jun. 13, 2014 and entitled ALARM FATIGUE MANAGEMENTSYSTEMS AND METHODS, the disclosures of each of which are herebyincorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure relates in general to the field of healthcare systemsand, more particularly, to systems and methods related to alarm fatiguemanagement.

BACKGROUND

The background description includes information that may be useful inunderstanding the present disclosure. It is not an admission that any ofthe information provided herein is prior art or relevant to thedisclosure, or that any publication specifically or implicitlyreferenced is prior art.

Alarm fatigue is desensitization to alarms brought on by overexposure toexcessive alarms, which can result in reduced response times or evencomplete failure to respond to critical issues that are raised by thealarms in the first place. Alarm fatigue is increasingly a seriousproblem in a variety of different industries and professions. Inparticular, medical professionals can experience alarm fatigue so severethat alarms indicating life threatening conditions are at timesoverlooked, resulting in numerous deaths each year. Various systemstailored towards modifying delivery of alarms exist in the healthcaremarketplace. One such system creates alert signals based on informationor data from medical systems. The alert signals can take the form ofmusic that is generated using the information or data from the medicalsystem, creating a wide variety of signals that simultaneously passinformation to an intended recipient. Another system reduces falsealarms in a certain predetermined region around a medical device. Amedical professional has a portable transmitter/monitor, and when analert condition exists, the system will check the physical proximity ofthe transmitter/monitor. In the event the transmitter/monitor is withinthe predetermined region defined as a false alarm region, the alert isconcealed.

In yet another example, portable alert devices deliver alerts to anintended recipient. The portable alert devices perform a scan to gatherrelevant information about the device's surroundings prior to issuing analert. In doing so, they can alter the mode of an alert depending on theenvironment that the portable devices and the intended recipient are in.In yet another example device, when a user is supposed to pump insulin,the device provides an alarm. To reduce alarm fatigue, the deviceincludes a randomization module that can generate random alarms.Randomness of alarms is determined by historical stability of the user'sblood glucose level. Thus, a user with a more stable blood glucose levelwill be rewarded with fewer alerts to check their blood glucose level.Another system creates “super-alarms.”

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure andfeatures and advantages thereof, reference is made to the followingdescription, taken in conjunction with the accompanying figures, whereinlike reference numerals represent like parts, in which:

FIG. 1 is a simplified block diagram illustrating a system for alarmfatigue management according to an example embodiment;

FIG. 2 is a simplified block diagram illustrating example details of thesystem according to an embodiment;

FIG. 3 is a simplified block diagram illustrating other example detailsof the system according to an embodiment;

FIG. 4 is a simplified block diagram illustrating yet other exampledetails of the system according to an embodiment;

FIG. 5 is a simplified block diagram illustrating yet other exampledetails of the system according to an embodiment;

FIG. 6 is a simplified block diagram illustrating yet other exampledetails of the system according to an embodiment;

FIG. 7 is a simplified flow diagram illustrating example operations thatmay be associated with an embodiment of the system;

FIG. 8 is a simplified block diagram illustrating yet other exampledetails of the system according to an embodiment;

FIG. 9 is a simplified flow diagram illustrating other exampleoperations that may be associated with an embodiment of the system; and

FIG. 10 is a simplified diagram illustrating example details accordingto an embodiment of the system.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

An apparatus for modifying alarms at a medical device for alarm fatiguemanagement is provided and includes an alarm monitor, an alarm filter,an alarm modifier, a memory element for storing data, and a processorthat executes instructions associated with the data, wherein theprocessor and the memory element cooperate such that the apparatus isconfigured for receiving an alarm condition from an alarm managementengine, the alarm condition based on an alarm fatigue level of a user ofthe medical device, the alarm fatigue level based on at least a userfatigue model, a medical device model and a patient condition, receivingan alarm from the medical device, modifying the alarm according to thealarm condition, the alarm condition being configured to increase alikelihood of the user responding to the modified alarm, and generatingan alarm indicator based on the modification.

EXAMPLE EMBODIMENTS

Turning to FIG. 1, FIG. 1 is a simplified block diagram illustrating asystem 10 according to an example embodiment. System 10 comprises anetwork 11 (generally indicated by an arrow) connecting an alarmmanagement engine 12 with one or more user devices 14 and medicaldevices 16. Alarm management engine 12 interfaces with a user fatiguemodel database 18, a medical device model database 20, and a patientconditions database 22. User fatigue model database 18 can include aplurality of user fatigue model 24 corresponding to different users orstakeholders of system 10. Each user fatigue model 24 can include one ormore user fatigue model attribute 26.

User fatigue model attribute 26 can comprise factors (e.g.,characteristics) that can affect a user's fatigue level. User fatiguemodel attribute 26 can include many different types of information,including information conveyed via status data 27 from user device 14and information entered into user fatigue model database 18 by othermeans, based on details about a particular user. In various embodiments,status data 27 can modify user fatigue model attribute 26 of userfatigue model 24. For example, user fatigue model attribute 26 comprisesa number of hours worked by the user; status data 27 can indicate thatthe user worked for 8 hours. In another example, user fatigue modelattribute 26 comprises a shift information for users on a particularfloor of a hospital; status data 27 can indicate that the shift is anight shift.

In a general sense, status data 27 comprises data corresponding tovarious measurable quantities related to a particular user. Status data27 can include physiological information (e.g., stress level (asmeasured by various measurable parameters such as blood pressure, heartrate, etc.), neurological activity, brain electrical activity, hormonelevels, body temperature, breathing rate, blood sugar level, age, changeof position, sleep activity (e.g., amount of sleep over previous 24hours and time since last sleep), etc.), alarm response timeinformation, user shift data (e.g., if user is not on active duty, usermay ignore alarm), environmental alarm data (e.g., number of alarmsreceived at user device), and various other parameters that can indicatea likelihood of the corresponding user responding to, or ignoring, analarm signal. In some embodiments, status data 27 can be communicatedwith an associated timestamp corresponding to a time of datacollection/measurement. In some embodiments, one or more correlationsbetween a physiological reaction and an alarm signal may be discerned.

User shift data can include various details describing the user's shift,including the user's work schedule (e.g., weekly schedule, monthlyschedule, yearly schedule, number of shifts worked without a day off,length of a shift, total time elapsed since the shift began, and/ornumber of shifts worked since the last vacation). For example, usershift data can indicate when a user works a late night shift followed byan early morning shift the next day. Status data 27 can also include anamount of user-directed alarms (e.g., the number of alarms the user hashad to respond to), alarm response time data (e.g., the user's time torespond), alarm severity for at least one user-directed alarm, a numberof repeated alarms (e.g., the number of alarms that have had to berepeated due to a delayed response or lack of a response), a number ofmissed alarms, a number of simultaneous alarms, a number of falsealarms, etc.

Environment alarm data can include the number of alarms the user hasbeen exposed to (e.g., both alarms directed to the user and alarmsdirected to other users), the loudness of an alarm the user has beenexposed to, the frequency of alarms the user has been exposed to duringa relevant time period (e.g., during a shift, during a day, during aweek, during two weeks, during a month, during a year, since time ofmost recent vacation, and since time of most recent day off), and atleast one alarm location (e.g., a location where the user heard analarm).

In some embodiments, user fatigue model attribute 26 can be changed(e.g., updated) periodically or continuously, depending on the samplingfrequency in an embodiment. Additionally, user fatigue model attribute26 can contain historical data (e.g., longitudinal study data). Someexamples of user fatigue model attribute 24 include personality factorsand physiological factors. Personality factors are factors that mightindicate a person is more or less prone to alarm fatigue (e.g.,irritability, psychological disorders, and personality type).Physiological factors can include factors that pertain to a certainuser's physical abilities (e.g., deafness, blindness, color blindness,sensitivity to particular light and/or sound patters, and varyingdegrees of disabilities related to these conditions and others).

Medical device model database 20 can include one or more medical devicemodel 28. Medical device model 28 can comprise a data construct (e.g.,an algorithmic model, a mathematical model, a digital model, analphanumeric identifier, etc.) of medical device 16, a representation ofan instrument reading (e.g., blood pressure measurement), or othersuitable function. Note that the term “data construct” as used hereinencompasses scalars, arrays, subarrays, matrices, vectors, and otherdata representations that are allocated in a region of memory in amemory element. The data constructs as used herein represent analoginformation (such as physical phenomena) with binary digits (or othercomputer interpretable representation) that are interpreted as integers,real numbers, characters, or other data types comprising a finite numberof discrete symbols that represent essentially infinite variation ofanalog information. In other words, the data construct represents aphysical entity in symbolic form, typically using symbols from arelatively small number of discrete enumerable symbols associated with acontext and other properties of the physical entity being represented.

In some embodiments, medical device model 28 can comprise a datacontainer for numerical values corresponding to device readings (e.g.,measurements). Each medical device model 28 can include one or morealarm condition parameter 30. For example, one of medical device model28 can comprise a model of a blood pressure monitor; alarm conditionparameter 30 for such model may correspond to a blood pressure readingthat indicates an alarm if the value of alarm condition parameter 30exceeds a predetermined threshold or other criteria. In someembodiments, the threshold may be included in corresponding medicaldevice model 28.

In another example, alarm condition parameter 30 comprises a thresholdvalue for a physiological measurement of a patient measured by medicaldevice 16. In yet another example, alarm condition parameter 30comprises a maximum frequency at which medical device 16 generates analarm. In yet another example, alarm condition parameter 30 comprises afrequency at which medical device 16 measures the patient. In yetanother example, alarm condition parameter 30 comprises measurementsensitivity corresponding to medical device 16's ability to measure thepatient. In various embodiments, each medical device 16 may berepresented by corresponding medical device model 28 in medical devicemodel database 20.

Patient conditions database 22 can include one or more patient condition32. By way of examples, and not as limitations, patient condition 32 mayinclude a data construct of an aggregate (e.g., collection, average,weighted average, composite, etc.) of health conditions, populationcharacteristics (e.g., lack of dexterity or memory with older users;high mental workload for anesthesiologists in operating rooms; etc.),diseases, symptoms (e.g., subjective patient symptoms and observablesymptoms such as temperature, analyte data, etc.), medications andvarious medical status. Examples of patient condition 32 includephysical or mental health parameter values, such as blood glucose indiabetes, respiratory flow in asthma, blood pressure in hypertension,cholesterol in cardiovascular disease, weight in eating disorders,T-cell or viral count in HIV, and frequency or timing of episodes inmental health disorders.

In a general sense, patient condition 32 is indicative of a context ofthe physiological measurement by medical device 16 (e.g., context underwhich the alarms are generated at medical device 16). For example,alarms generated at medical device 16 may indicate a hypertension statusof a patient when the alarms are directed towards, or result as aconsequence of, high blood pressure of the patient. In another example,alarms generated at medical device 16 may indicate a serious conditionof the patient when the alarms are directed towards, or result as aconsequence of, various disparate physiological measurements of thepatient.

In another example, patient condition 32 can refer to a medical statusof the patient (e.g., “undetermined” may correspond to a status of apatient awaiting physician and assessment; “good” may indicate thatvital signs (e.g., pulse rate, oxygen levels, blood pressure, etc.) arestable and within normal limits, with the patient being conscious andcomfortable; “fair” may indicate that vital signs are stable and withinnormal limits with the patient being conscious, but uncomfortable;“serious” may indicate that vital signs are unstable and not withinnormal limits, with the patient being acutely ill; “critical” mayindicate that vital signs are unstable and not within normal limits withthe patient being unconscious; “deceased” may indicate that the patientis dead). In another example, patient condition 32 of “hypertension” mayindicate that the patient suffers from hypertension, with expected highblood pressure readings; patient condition 32 of “diabetes” may indicatethat the patient suffers from diabetes with higher than normal bloodsugar readings without medication, etc. In some embodiments, patientcondition 32 may correspond to a mathematical model (e.g., polynomialfunction, alphanumeric value, matrix, etc.) of a corresponding physicalor mental health condition.

In some embodiments, alarm management engine 12 receives, in addition touser device status data 27, one or more alarm indicator 34 from one ormore medical device 16. Alarm indicator 34 can indicate modifications toalarms generated at medical device 16, and can serve as feedbackregarding the modifications (e.g., whether the modifications areeffective). In some embodiments, alarm indicator 34 can also includesignals (e.g., auditory, tactile, optical, vibratory, electrical,wireless, and/or other signals) that are indicative of, or associatedwith alarms configured on medical device 16. Alarm management engine 12may include, in addition to a processor 36 and a memory element 38, analarm fatigue calculator 40 and an alarm condition calculator 42. Alarmmanagement engine 12 may be coupled to (and communicate with) adapter 46associated with respective medical device 16.

During operation, alarm management engine 12 may receive status data 27from user device 14. Status data 27 can be collected substantiallycontinuously in some embodiments, and periodically (e.g., every 1, 5,10, 15, 20, 30, 45 second and/or every 1, 5, 10, 15, 30, 45 minutes) inother embodiments. In some embodiments, status data 27 may be collectedin predetermined time intervals or ranges (e.g., every 0-5 seconds, 5-10seconds, 10-15 seconds, 15-20 seconds, 20-30 seconds, 30-45 seconds,45-60 seconds and/or every 0-5, 5-10, 10-15, 15-30, or 30-45 minutes).Additionally, status data 27 may be collected on a “push” basis (e.g.,in response to certain triggers such as alerts and responses to alerts)with user device 14 pushing status data 27 to alarm management engine12. Alarm fatigue calculator 40 may calculate alarm fatigue levelsassociated with respective users of user device 14 based on one or moreuser fatigue model 24 in user fatigue model database 18.

Alarm fatigue calculator 40 may feed the calculated alarm fatigue levelto alarm condition calculator 42. Alarm condition calculator 42 maycalculate alarm condition 44 for medical device 16 based on informationfrom status data 27, alarm indicator 34, calculated alarm fatigue level,medical device model 28, and patient condition 32. In variousembodiments, alarm condition 44 comprises instructions for alarmsgenerated by medical device 16. The instructions can specify alarmattributes (e.g., alarm frequency, alarm threshold, alarm distributionmode (such as visual, auditory, sensory, tactile, etc.), alarm intensity(e.g., for a given distribution mode, how intensely the alarm ispresented), alarm duration, alarm severity, etc.), alarm outputinterface (e.g., on user device 14, on a centralized alarm system, ormedical device 16, etc.), measurement sensitivity, and any otherparameter that affects likelihood of increased response to the alarms byone or more users whose alarm fatigue levels are considered ingenerating alarm condition 44.

Alarm condition 44 may be communicated to an adapter 46 located atapplicable medical device 16. Adapter 46 may monitor and filter alarmsfrom medical device 16 based on alarm condition 44. In some embodiments,medical device 16 and/or adapter 46 may generate and communicate alarmindicator 34 to alarm management engine 12. Alarm indicator 34 canprovide feedback about alarm condition 44, which can be used in machinelearning algorithms.

In some embodiments, status data 27 can indicate that an alarm has beenfalsely generated. Alarm management engine 12 can use the false alarmindication information to learn conditions that lead to false alarmsgeneration. To achieve condition learning, embodiments of alarmmanagement engine 12 can implement various machine learning algorithms.For example, some embodiments implement supervised learning techniques,such as Averaged One-Dependence Estimators (AODE), artificial neuralnetwork, Bayesian statistics, case-based reasoning, decision trees,inductive logic programming, Gaussian process regression, geneexpression programming, group method of data handling, learningautomata, learning vector quantization, logistic model tree, minimummessage length, lazy learning, instance-based learning, probablyapproximately correct learning, ripple down rules, symbolic machinelearning, sub-symbolic machine learning algorithms, support vectormachines, random forests, ensembles of classifiers, bootstrapaggregating (bagging), boosting (meta-algorithm), ordinalclassification, regression analysis, information fuzzy networks (IFN),and conditional random field. Suitable sources for machine learningsystems include those available at www.scikit-learn.org), which offersnumerous machine learning, data mining, and data analysis tools based onthe Python computer language.

In other embodiments, alarm management engine 12 can also (e.g.,alternatively, or additionally) implement unsupervised learningtechniques, such as artificial neural network, data clustering,expectation-maximization algorithm, self-organizing map, radial basisfunction network, vector quantization, generative topographic map,information bottleneck method, association rule learning, Apriorialgorithm, Eclat algorithm, Frequent Pattern (FP)-growth algorithm,hierarchical clustering, single-linkage clustering, conceptualclustering, partitional clustering, k-means algorithm, fuzzy clustering,Density-Based Spatial Clustering of Applications with Noise (DBSCAN),Ordering Points To Identify the Clustering Structure (OPTICS) algorithm,outlier detection, local outlier factor, reinforcement learning,temporal difference learning, q-learning, learning automata, Monte Carlomethod, Sarsa, deep learning, deep belief networks, deep Boltzmannmachines, deep convolutional neural networks, and deep recurrent neuralnetworks.

In embodiments, one or more machine learning algorithms can be used tocreate a smart alarm management system that can develop rules for alarmgeneration. For example, alarm indicator 34 and/or status data 27indicative of a false alarm can be aggregated over a period of timeallowing a machine learning algorithm executing in alarm managementengine 12 to learn when an alarm is more or less likely to be a falsealarm. Over time, the algorithm can additionally learn when to preventgeneration of an alarm based on historical information and trends learntfrom alarm indicator 34 and status data 27. In another example, a smartalarm system could be used to determine whether an alarm should begenerated based on data collected from plurality of medical device 16.

In some embodiments, alarm management engine 12 can implement one ormore machine learning algorithms to develop prediction models foralarms. For example, alarm management engine 12 can provide a projectednumber of false alarms for a particular shift and it can take thecalculated projected number of false alarms into account by creatingrules for generating alarms throughout that shift.

In some embodiments, users can manually input status data to the system.For example, a user can report dreams or nightmares. A user can alsoreport diet, exercise, and different types of ailments (e.g., if a usergets the flu, the user could report that to the system). In variousembodiments, alarm management engine 12 can receive a variety ofuser-created status data inputs in status data 27 that may be used tocreate better (e.g., more productive, useful, etc.) user fatigue models24.

In some embodiments, user fatigue model 24 is updated based on statusdata 27. Multiple user fatigue models 24 can be updated simultaneouslyor in sequence as status data 27 is received; in some embodiments, alarmmanagement engine 12 may be limited in the updating by the number ofusers registered thereto. In some embodiments, incoming status data 27can overwrite previously stored status data within user fatigue model24; in other embodiments, incoming status data 27 can be stored alongwith previously stored status data to create a historical set of statusdata.

In various embodiments, the alarm fatigue level may be calculated basedon previously updated user fatigue model 24. Alarm fatigue level canhave a baseline and a current value. For example, one or more usersmight have a slowly increasing alarm fatigue level week over week.However, daily factors impacting the user can alter (e.g., add to orsubtract from) the base line. For example, a base line can increase by2% week over week. In another example, a user is alert with a reducedfatigue relative to the baseline in the morning, but after a 12 hourshift the user's fatigue level is heightened relative to that user'sbaseline. A baseline might be reset after vacation, for example.

A “default” user fatigue model 24 for a particular user can comprise thebaseline in some embodiments. The baseline can be a historicallydetermined “normal” fatigue level for the particular user based onsamples collected during a day, a week, a particular shift, and/or aparticular length of shift, all of which serve to determine an“un-fatigued” state. For example, as the day progresses, user fatiguemodel 24 can be updated based on various status data 27 received and theuser's alarm fatigue level can subsequently be determined. As userfatigue model 24 of the corresponding user is updated, the alarm givento the user can be adapted accordingly. Consider a scenario where auser, say a nurse or doctor, has numerous long shifts. The disclosedsystem might discover that the user becomes fatigued with respect toalarms after more than 10 hours of shift on a consistent basis. Thesystem discovers this condition by measuring the user's response time toalarms where the response time begins to increase measurably after 10hours. Even though the user is responding within an acceptable period,the measured increase in response time can be considered as a leadingindicator for fatigue. Discovery of this leading indicator can then beconsidered part of the specific user's baseline fatigue model in futuresettings. Further, such a measured indicator can also be incorporatedinto the fatigue models of other stakeholders having similarcharacteristics of the user; say all nurses or all doctors for example,if the measured indicator is validated for the class of users.

Using status data 27, some embodiments alarm management engine 12 canadapt and learn how quickly a particular user's alarm fatigue levelincreases over time given normal operating (e.g., working) conditions.Other factors that can be learned over time include how the user's alarmfatigue level changes after some number of days off, after a weekend,and/or after a night of sleep (e.g., where the user gets only 6 hours ofsleep compared to when the user gets 10 hours of sleep).

In some embodiments, the alarm fatigue level can be categorized intodifferent tiers, such as no alarm fatigue, low-level alarm fatigue,mid-level alarm fatigue, and high-level alarm fatigue. In otherembodiments, the alarm fatigue level can be classified along acontinuum. When alarm fatigue is classified along a continuum, alarmsgenerated may be modified based on user fatigue model attributes 26 seenas contributing more to alarm fatigue levels. For example, if theparticular user experiences alarm fatigue caused mainly by a high numberof environment alarms, alarm management engine 12 can provide the userwith specially tailored alarms. In another example, if the user hasexperienced a high number of alarms that incorporate particular sounds,the system can provide an alarm that includes a different set of soundsthat may also incorporate tactile feedback and/or visual feedback. Userfatigue attributes 26 can be weighted according to importance orstrength of correlations in some embodiments. In some embodiments, alarmfatigue levels can be characterized on different scales, such aspersonalized fatigue level (e.g., specific to a particular user), agroup level (e.g., specific to users working in a particular group), a“floor” level (e.g., specific to users working in a particular floorlevel of a building), a “shift” level (e.g., specific to users workingin a particular shift), an institution level (e.g., specific to usersworking in a particular institution), etc. In some embodiments, alarmfatigue levels can have many dimensions beyond a tiered system. Such anapproach can be considered advantageous, for example, because it allowsthe system to deliver alarms to the user where the alarm not onlyaddresses the user's fatigue, but also provides an indication to theuser that they are, in fact, suffering from fatigue based on themodality or nature of the alarm.

In a general sense, fatigue data can be classified into various types,examples include emotional fatigue, physical fatigue, or mental acuityfatigue. Each fatigue-type can be inferred based on status data 27. Forexample, mental acuity fatigue may be inferred by determining how longit takes the user to make a decision; physical fatigue may be inferredbased on how long it physically takes the user to respond to an alarm orother activity (e.g., where response time can optionally be normalizedbased on distance traveled to respond to an alarm); etc. Each type offatigue may correspond to a unique signature or template generated basedon status data 27.

In various embodiments, alarm indicator 34 comprises an indication of amedical alert requiring a response from the user. In some embodiments,alarm indicator 34 may comprise inputs from medical device 16 thatcollect physiological information from the patient. When, for example, amedical sensor indicates a heart rate has flat lined, an alert conditionis met. Alarm management engine 12 receives an indication of the alertcondition through alarm indicator 34.

According to some embodiments, alarm management engine 12 may be capableof synthesizing and/or interpreting medical sensor data directly, andthen determining whether an indication of a medical alert exists. Inembodiments where the alarm management engine 12 is used with existingmedical device 15, alarm management engine 12 may learn and/or adapt sothat alarm condition 44 can be standardized (e.g., made uniform) andmanaged regardless of the legacy and/or default alarm conditions used bymedical device 16. In some embodiments, learning models can be tied topatient condition 32, care setting, device type, device manufacturer,and other such parameters.

In various embodiments, alarm management engine 12 facilitatesgenerating an alarm directed to a specific user taking into account theuser's alarm fatigue level. In some embodiments, alarm management engine12 additionally takes into account the alarm fatigue level of anotheruser or users. In some embodiments, an alarm generation instruction istriggered by an indication of a medical alert from an outside source;thereafter, the alarm generation instruction is modified by one or morealarm fatigue levels; the modified alarm generation instructiongenerates the alarm. In various embodiments, alarm management engine 12can take into account alarm fatigue levels of a single user; in otherembodiments, alarm management engine 12 can take into account the alarmfatigue of a plurality of users. In various embodiments, alarmmanagement engine 12 can also take into account alarm fatigue levels ofnon-users (e.g., persons outside the system may develop alarm fatigue,which can minimize the impact of the alarm based on the number ofnon-users potentially affected).

According to some embodiments, alarm management engine 12 can take intoaccount instructions that specific users should receive only particularalerts (e.g., some recipients can only interpret sound alerts generatedin a particular frequency range). In various embodiments, system 10 canbe configured to comply with various regulations (e.g., governmental,administrative, and private) with which alarm systems must comply.

In some embodiments, when alarm fatigue level indicates no alarmfatigue, alarm condition 44 may permit various alarms to be generated(e.g., issued, created, sounded, etc.), for example, alarms based onsounds, vibrations, and/or a light, where the alarm corresponds to a“default” state. When alarm fatigue level indicates low-level alarmfatigue, alarm condition 44 may indicate a different alarm, for example,a specific combination of sounds, vibrations, and/or lightscorresponding to the low-level alarm fatigue. Likewise, mid- andhigh-level alarm fatigue levels may correspond to respective alarmtypes, such as unique sounds, lights, etc. tailored to alarm fatiguelevels of relevant users.

In some embodiments, the generated alarm may be distributed (e.g.,disseminated, communicated, transmitted, sounded, etc.) based on alarmcondition 44. Distribution can be carried out in a number of differentways. For example, each individual user may have a personal alarm device(e.g., watch) that is capable of conveying an alarm perceptible to theintended recipient. In some embodiments, the distributed alarm isdirected to relevant users to the exclusion of others.

In an example scenario using embodiments of system 10, consider ahypothetical intensive care unit (ICU) of a hospital, which is manned bytwo nurses, Alice and Bob. At 8:00 AM, nurse Alice logs into user device14 (e.g., computer) at a central hub in the ICU to begin her day'sshift. Status data 27 logs the time of entry and communicates it withNurse Alice's credentials to alarm management engine 12. Alarm fatiguecalculator 40 calculates nurse Alice's user fatigue at level I (e.g.,lowest level) based on user fatigue model 24 and user fatigue modelattribute 26 determined from status data 27.

Assume that at 8:15 AM, a patient is brought into the ICU in seriouscondition. Nurse Alice registers the patient at the central hub andstatus data 27 indicative of the patient's condition is manually enteredinto user device 14 and received at alarm management engine 12. NurseBob logs into an electronic chart at the patient's bedside and entersinformation about medications being provided to the patient into theelectronic chart. Information entered by nurse Bob along with nurseBob's credentials are sent to alarm management engine 12. Assume thatNurse Bob is set to end his shift at 9:00 AM after 8 hours of continuouswork at the ICU. Alarm fatigue calculator 40 calculates nurse Bob's userfatigue at level V (e.g., highest level) at the time of last receipt ofstatus data 27 based on user fatigue model 24 and user fatigue modelattribute 26 determined from status data 27.

Assume that medical device 16 substantially continuously measures thepatient's blood pressure. A blood pressure reading at 8:16 AM indicates130/90 mm Hg; at 8:20 AM indicates 120/80 mm Hg; and at 8:24 AMindicates 115/65 mmHg. Medical device model 28 corresponding to medicaldevice 16 may specify that any blood pressure measurement over 120/80 mmHg should generate an alarm. Patient condition 32 may specify that bloodpressure for patients with a “serious” designation typically can varybeyond the normal range, but the rate of change is to be monitored moreclosely. Further in view of Bob's inferred high user fatigue level,generation of alarms may be tempered to only the more significant ones.

Alarm condition calculator 42 generates alarm condition 44 based oninformation from medical device database 20, patient conditions database22 and user fatigue calculated by alarm fatigue calculator 40, andcommunicates alarm condition 44 to an adapter 46 located at medicaldevice 16. Adapter 46 monitors and filters alarms from medical device16. In the example, alarms may not be generated at medical device 16 forblood pressure readings at 8:16 AM and 8:20 AM; however, alarms may begenerated at 8:24 AM in view of the rate of change of blood pressure. Insome embodiments, alarms may be generated at 8:16 AM and communicated tonurse Alice's work station, as nurse Alice has a lower inferred fatiguelevel than nurse Bob, but not displayed visibly or audibly at nurseBob's work station, whereas alarms generated at 8:24 AM may becommunicated to both nurse Alice and nurse Bob. Medical device 16 and/oradapter 46 may generate alarm indicator 34 indicating that alarms havebeen generated and/or communicated (e.g., based on alarm condition 44).Such alarm indicator 34 can facilitate feedback and/or machine learningat alarm management engine 12.

In a general sense, alert management engine 12 of system 10 configuredto perform various steps. First, alert management engine 12 receivesstatus data 27 corresponding to a user, where status data 27 modifies atleast one user fatigue model attribute 26. Second, alert managementengine 12 updates corresponding user fatigue model 24 based on statusdata 27. Third, alert management engine 12 determines an alarm fatiguelevel based on updated user fatigue model 24. Fourth, alert managementengine 12 generates alarm condition 44 targeting the user based on thecalculated alarm fatigue level (among other parameters). Finally,adapter 46 modifies alarms generated at medical device 16 according toalarm condition 44 and distributes modified alarm 78 to the user.

In various embodiments, system 10 may facilitate aggregation and/ormanagement of alarms in a consumable fashion. In some embodiments,adapter 46 associated with medical device 16 may comprise an alarmmonitor, an alarm filter, an alarm modifier, a memory element forstoring data, and a processor that executes instructions associated withthe data. The processor and the memory element cooperate such thatadapter 46 is configured for receiving alarm condition 44 from alarmmanagement engine 12, receiving an alarm from medical device 16,modifying the alarm according to alarm condition 44 and generating alarmindicator 34 based on the modification. In some embodiments, alarmindicator 34 comprises a feedback to alarm management engine 12, andalarm condition 44 is further based on the feedback. Alarm condition 44may be configured to increase a likelihood of the user responding to themodified alarm.

In some embodiments, alarm condition 44 is based on an alarm fatiguelevel of a user of medical device 16, the alarm fatigue level beingbased at least on medical device model 28 and patient condition 32. Thealarm may be based on a physiological measurement of a patient bymedical device 16. In some embodiments, modifying the alarm can includedeleting the alarm based on alarm condition 44. In some otherembodiments, modifying the alarm can include changing a format of thealarm. In yet other embodiments, modifying the alarm can includechanging a distribution mode of the alarm.

Turning to the infrastructure of system 10, alarm management engine 12can be embodied as computer executable instructions stored on one ormore non-transitory computer-readable media (e.g. hard drives, opticalstorage media, flash drives, ROM, RAM, etc.) that, when executed by oneor more processors, cause the processors to execute the functions andprocesses described herein. In some embodiments, alarm management engine12 may execute in a distributed manner, portions of which are integratedinto different adapters 46. For example, a portion of alarm managementengine 12 that can calculate alarm condition 44 for medical device A maybe integrated into adapter 46 corresponding to medical device A; anotherportion of alarm management engine 12 that can calculate alarm condition44 for medical device B may be integrated into adapter 46 correspondingto medical device B; and so on. In other embodiments, alarm managementengine 12 may execute in a centralized manner, calculating alarmcondition 44 for a plurality of medical devices 16 and communicatingalarm condition 44 over network 11 to relevant medical devices 16.

In some embodiments, alarm management engine 12 can be integrated into asingle computing device or distributed among a plurality of computingdevices (either locally or remotely located from one another)communicatively coupled via data exchange interfaces (e.g., short-range,long-range, wireless, wired, near-field communication, Bluetooth,Ethernet, Wi-Fi, USB, etc.), and/or connected via local or long-rangenetworks (e.g. Internet, cellular, local-area networks, wide-areanetworks, intranets, etc.). In some embodiments, alarm management engine12 can be embodied as one or more dedicated hardware computing devicesspecifically programmed (e.g. via firmware) to execute the functions andprocesses described herein.

In some embodiments, alarm management engine 12 can be incorporated intoexisting alarm management systems via installation of appropriatehardware and/or software updates associated with the functions describedherein. For example, one suitable alarm management system isNantHealth's Magellan™ or cOS™ system, including functionalities asdescribed herein.

Turning to FIG. 2, FIG. 2 is a simplified block diagram illustratingnetwork 11 according to an example embodiment of system 10. Alarmmanagement engine 12 may execute in a server 50 connected over a localarea network 52 with medical device 16 and user device 14. Server 50 mayconnect over Internet 54 to a clinical operating system (cOS) 56 thatmay communicate with user fatigue model database 18, medical devicemodel database 20 and patient conditions database 22. Note that LAN 52and Internet 54 may be connected over a router (not shown) situated atan edge of LAN 52. In some embodiments, LAN 52 may comprise a portion ofan enterprise network.

In various embodiments, cOS 56 integrates clinical, financial,operational and environmental data into a single platform. In variousembodiments, cOS 56 comprises a cloud-based platform for patientrecords, medical devices, imaging systems, financial systems, costingsystems, evidence-based clinical pathways, and personalized genomic andproteomic data. cOS 56 combines and organizes pertinent medicalinformation for easy access and utilization at the point of care. Invarious embodiments, alarm management engine 12 may connect to cOS 56over Internet 54, and access user fatigue model 24, patient condition32, medical device model 28, etc. through cOS 56. cOS 56 can facilitateprivacy of health records, appropriate authentication, and othersecurity measures consistent with various health care privacy relatedlaws and regulations. For example, alarm management engine 12 mayestablish a secure tunnel with cOS 56 to access user fatigue modeldatabase 18, medical device model database 20 and patient conditionsdatabase 22.

In some embodiments, server 50 may comprise a physical computing device,such as a desktop server. In other embodiments, server 50 may execute ina rack server, for example, in a computing station located remotely frommedical device 16 and user device 14, but nevertheless in communicationwith medical device 16 and user device 15 over LAN 52. LAN 52 mayinclude switches and other network elements that can facilitate wired orwireless communication between medical device 16, user device 14 andalarm management engine 12.

Turning to FIG. 3, FIG. 3 is a simplified block diagram illustratingnetwork 11 according to another example embodiment of system 10. Alarmmanagement engine 12 may execute in a server 50 connected over a firstlocal area network 52 (LAN A) with medical device 16 and user device 14.Server 50 may connect over a second local area network 52 (LAN B) touser fatigue model database 18, medical device model database 20 andpatient conditions database 22. In some embodiments, LAN A and LAN B maycomprise the same LAN; in other embodiments, LAN A and LAN B maycomprise separate portions of an enterprise network; in yet anotherembodiment, LAN A and LAN B may comprise separate enterprise networkslinked together by appropriate secure network elements, such as switchesand routers; in yet other embodiments, LAN A and LAN B may comprisevirtual local area networks (VLANS) of an enterprise network.

Note that various implementations of LAN A and LAN B may be encompassedby the broad scope of the embodiments. In one example embodiment, LAN Aand LAN B may be connected over a wireless network. In anotherembodiment, LAN A and LAN B may be connected over the Internet (or otherWAN), but through secure tunnels and/or other network security measurestailored to transparently and seamlessly cause LAN B to appear to belongto the same network as LAN A. For example, user fatigue model database18, medical device model database 20 and patient conditions database 22may be stored and/or implemented in LAN B, comprising a data centernetwork geographically or physically separate from LAN B, comprising ahospital network, but nevertheless connected to LAN A throughappropriate communication channels.

Turning to FIG. 4, FIG. 4 is a simplified block diagram illustratingnetwork 11 according to yet another example embodiment of system 10.Alarm management engine 12 may communicate with medical device 16 anduser device 14 across Internet 54 through cOS 56. For example, cOS 56may execute in a cloud network and receive status data 27, alarmindicator 34, etc. from medical device 16 and user device 14,communicate them with alarm management engine 12, and facilitate accessby alarm management engine 12 of user fatigue model database 18, medicaldevice model database 20 and patient conditions database 22stored/implemented in the cloud. In some embodiments, alarm managementengine 12 may execute in a server communicatively coupled to cOS 56; inother embodiments, alarm management engine 12 may execute in a sameserver as cOS 56. In some embodiments, user fatigue model database 18,medical device model database 20, and patient conditions database 22 maybe implemented in a separate storage area network (SAN) that iscommunicatively coupled to cOS 56 and/or alarm management engine 12.

Turning to FIG. 5, FIG. 5 is a simplified block diagram illustratinganother example embodiment of system 10. In some embodiments, alarmcondition 44 may be provided based on subscriptions to an alarm service.A subscriptions database 58 may be communicatively coupled to alarmmanagement engine 12. Subscriptions database 58 may include a medicaldevice group-subscription level table 60 and subscription levels 62. Aplurality of medical device 16 may be grouped into separate medicaldevice groups 64. Alarm management engine 12 may access medical devicegroup-subscription level table 60 in subscriptions level database 58 andgenerate alarm condition 44 based on subscription levels 62 (in additionto other parameters as already discussed herein).

For example, medical devices C and D may be grouped into medical devicegroup A; medical devices E and F may be grouped into medical devicegroup B. Medical device groups 64 may be classified (e.g., grouped)according to any suitable grouping attribute. For example, the groupingattribute may comprise location of medical device 16: medical devicegroup A may correspond to medical devices located in an ICU of ahospital; medical device group B may correspond to medical deviceslocated in an outpatient clinic of the hospital. In another example, thegrouping attribute may comprise device types: medical device group A maycorrespond to heart monitors; medical device group B may correspond toblood pressure monitors. In yet another example, the grouping attributemay comprise departments: medical device group A may comprise medicaldevices belonging to the oncology department; medical device group B maycomprise medical devices belonging to the emergency room department.Virtually any suitable grouping attribute may be included within thebroad scope of the embodiments.

Each medical device group 64 may subscribe to different subscriptionlevels 62. Each subscription level 62 may specify certain types of alarmcondition 44; certain types of analyses used to generate alarm condition44; certain attributes (e.g., parameters, variables, etc.) to be takeninto consideration to compute alarm condition 44; etc. Virtually anysuitable differentiator of alarm condition 44 may be included insubscription level 62 within the broad scope of the embodiments.

During operation, assume, merely for example purposes and not as alimitation, that alarm management engine 12 generates alarm condition Ofor identical medical devices C and E based on alarm fatigue levels,medical device model 28, patient condition 32, etc., as describedherein. Assume that alarm condition O indicates that alarms are to begenerated for users P and Q only when a patient's blood pressure crosses130/90 mm Hg.

Alarm management engine 12 may access subscriptions database 58 todetermine subscription level 62 corresponding to medical devices C andE. For example, alarm management engine 12 may determine (e.g., frompreconfigured information, polling medical devices C and E, or otherappropriate methods) that medical device C belongs to medical devicegroup A and medical device E belongs to medical device group B. Frommedical device group-subscription level table 60, alarm managementengine 12 may determine that medical device group A subscribes tosubscription level G, which indicates that alarm signals be communicatedin a vibratory mode to specific user devices such as watches (accordingto subscription level 62) and medical level group B subscribes tosubscription level H, which indicates that alarm signals be communicatedaudibly and visually on the medical device itself.

Alarm management engine 12 may modify alarm condition Oto alarmcondition 1 for medical device C based on information from subscriptionsdatabase 58; and modify alarm condition Oto alarm condition 2 formedical device E. Thus, alarm condition 1 may indicate that alarms areto be generated for users P and Q only when the patient's blood pressurecrosses 130/90 mm Hg and the users are to be notified via vibratory modeto specific user devices; alarm condition 2 may indicate that alarms areto be generated for users P and Q only when the patient's blood pressurecrosses 130/90 mm Hg and the alarms are to be audibly and visuallyactivated at medical device E.

Turning to FIG. 6, FIG. 6 is a simplified block diagram illustratingexample details according to an example embodiment of system 10. Adapter46 may be communicatively coupled to medical device 16 and alarmmanagement engine 12. In some embodiments, adapter 46 may be remote frommedical device 16 and communicate wirelessly with medical device 16. Inother embodiments, adapter 46 may be detachably attached (e.g., throughuniversal serial bus (USB) or other such electrical connectors) tomedical device 16. In yet other embodiments, adapter 46 may bepermanently attached to medical device 16. In yet other embodiments,adapter 46 may comprise a separate physical appliance (e.g., component)embedded inside a chassis of medical device 16 and integrated therein.In yet other embodiments, adapter 46 may comprise an inseparablecomponent integrated into a hardware of medical device 16; for examplemedical device 16 could integrate a Digi Connect ME® device that hasbeen configured or programmed according to the disclosed techniques.

In yet other embodiments, adapter 46 may comprise a software applicationexecuting inside medical device 16. In yet other embodiments, adapter 46may comprise a software application executing in a computing deviceseparate from medical device 16 and communicatively coupled thereto(e.g., connected via Ethernet links, connected through a controller,etc.). For example, adapter 46 may comprise a software application(e.g., software container, object, module, etc.) executing alongside(e.g., with, concurrently, etc.) alarm management engine 12 in the sameserver.

Adapter 46 may include a processor 66, a memory element 68, an alarmmonitor 70, and an alarm filter 72, an alarm modifier 74. Adapter 46 mayreceive alarm condition 44 from alarm management engine 12. Alarmcondition 44 may be based on an alarm fatigue level of a user of medicaldevice 16, the alarm fatigue level being based on (among otherparameters), user fatigue model 24, medical device model 28 and patientcondition 32.

Adapter 46 may also receive one or more alarms 76 generated by medicaldevice 16. In some embodiments, alarm 76 can comprise electricalsignals; in other embodiments, alarm 76 can comprise visual, auditory,vibratory and other kinds of signals; in yet other embodiments, alarm 76can comprise a signal indicative of an alarm; in yet other embodiments,alarm 76 comprises signals instructing an alarm to be generated. Alarmmonitor 70 at adapter 46 is configured to receive and identify alarms76. For example, alarm 76 comprises an electrical signal and alarmmonitor 70 may comprise an electrical component configured to change itsproperty upon receipt of the electrical signal. In another example,alarm 76 comprises an audible signal, and alarm monitor 70 may comprisea microphone sufficiently sensitive to pick up the audible signal. Inyet another example, alarm signal comprises an optical signal, and alarmmonitor 70 may comprise a light sensitive component that may activateupon receipt of the optical signal. Virtually any suitable type of alarmmonitor 70 that can interface with alarms 76 may be used within thebroad scope of the embodiments.

In various embodiments, adapter 46 modifies alarms 76 to modified alarm78 according to alarm condition 44. Note that alarm condition 44 may beconfigured to increase a likelihood of the user (of medical device 16)responding to modified alarm 78 (e.g., relative to a likelihood of theuser responding to alarms 76). Modification may comprise filteringalarms 76 to delete some of alarms 76 based on alarm condition 44 (e.g.,alarm condition 44 may specific alarms once every 10 minutes so that anyalarms generated in the intervening 9 minutes are discarded);modification may comprise altering a format of alarms 76 (e.g., changinga vibratory format to a visually blinking format; changing a textmessage alarm to a beeping sound alarm; changing alarm 76 comprising analarm instruction to a vibration on the user's wearable device; etc.).In some embodiments, modification may comprise changing a distributionmode of alarms 76. For example, alarms 76 may comprise a blinking lighton medical device 16; modified alarm 78 may comprise a text message oncell phones of a plurality of registered users.

In some embodiments, modification of alarms 76 may be accomplished byalarm filter 72 and alarm modifier 74. Alarm filter 72 is configured tofilter alarms 76 according to alarm condition 44. For example, medicaldevice 16 may generate alarms 76 indiscriminately whenever certainmanufacturer preconfigured alert conditions are met. On the other hand,alarm condition 44 may be tailored to specific users, environments, etc.taking into consideration alarm fatigue levels, and other factors asdescribed herein. Accordingly, alarm filter 72 may reduce the number ofalarms; change the type of alarms; or otherwise generate instructions tochange alarms 76 in a suitable manner according to alarm condition 44.In some embodiments, alarm filter 72 may take as inputs signalscorresponding to alarms 76 from alarm monitor 70 and alarm condition 44from alarm management engine 12 and produce as output instructions togenerate modified alarm 78.

Alarm modifier 74 may generate modified alarm 78 according to theinstructions from alarm filter 72. In some embodiments, each modifiedalarm 78 may also cause generation of alarm indicator 34, which may besent back to alarm management engine 12 (e.g., as feedback). In someembodiments, alarm indicator 34 may be used to compute alarm condition44.

Turning to FIG. 7, FIG. 7 is a simplified flow diagram illustratingexample operations 80 that may be associated with adapter 46 accordingto an embodiment of system 10. At 82, adapter 46 receives alarmcondition 44 from alarm management engine 12. In some embodiments, alarmcondition 44 is based on an alarm fatigue level of a user of medicaldevice 16, the alarm fatigue level based on at least user fatigue model24, medical device model 28, and patient condition 32. At 84, adapter 46receives alarm 76 from medical device 16. At 86, adapter 46 modifiesalarm 76 to generate modified alarm 78. In various embodiments, alarmcondition 44 is configured to increase a likelihood of the userresponding to modified alarm 78. At 88, adapter 88 generates alarmindicator 34 based on the modification.

Turning to FIG. 8, FIG. 8 is a simplified block diagram illustratingexample details of a system 100 for alarm fatigue management. System 100includes two components: a database 102, which can be stored eitherlocally or remotely, and an alarm management engine 116. Database 102can include N user fatigue models 104, 106, 108, where N is an integervalue corresponding to the number of users within system 100. Each userfatigue model can include one or more user fatigue model attributes.User fatigue model 104 holds, for example, M user fatigue modelattributes 110, 112, and 114, where M is an integer value correspondingto the number of attributes held in the user fatigue model.

Alarm management engine 116 can receive alarm indications via input 118,and it can additionally communicate with J number of user devices whereJ is an integer value corresponding to the number of user devices usedwith system 100.

Alarm management engine 116 can be embodied as computer executableinstructions stored on one or more computer-readable media (e.g. harddrives, optical storage media, flash drives, ROM, RAM, etc.) that, whenexecuted by one or more processors, cause the processors to execute thefunctions and processes of the inventive subject matter. In someembodiments, alarm management engine 116 can be integrated into a singlecomputing device or distributed among a plurality of computing devices(either locally or remotely located from one another) communicativelycoupled via data exchange interfaces (e.g., short-range, long-range,wireless, wired, near-field communication, Bluetooth, Ethernet, Wi-Fi,USB, etc.), connected via local or long-range networks (e.g. Internet,cellular, local-area networks, wide-area networks, intranets, etc.). Insome embodiments, alarm management engine 116 can be embodied as one ormore dedicated hardware computing devices specifically programmed (e.g.via firmware) to execute the functions and processes of the inventivesubject matter.

In some embodiments, alarm management engine 116 can be incorporatedinto existing alarm management systems via the installation of requiredhardware and/or software updates associated with the functions of theinventive subject matter. For example, one suitable alarm managementsystem is NantHealth's Magellan™ of cOS™ system.

User fatigue model attributes can be considered to be factors orcharacteristics that can affect a user's fatigue level. User fatiguemodel attributes can include many different types of information,including information conveyed via the status data and information thatis set based on details about a particular user. These attributes can bechanged and/or updated periodically or continuously, depending on thesampling frequency in an embodiment. Additionally, the attributes cancontain historical data rather than repeatedly updating a single pieceof information.

Some examples of user fatigue model attributes include personalityfactors and physiological factors. Personality factors are factors thatmight indicate a person is more or less prone to alarm fatigue (e.g.,irritability, psychological disorders, and personality type).Physiological factors can include factors that pertain to a certainuser's physical abilities (e.g., deafness, blindness, color blindness,sensitivity to particular light and/or sound patters, and varyingdegrees of disabilities related to these conditions and others).

In some embodiments, system 100 comprising database 102 and alertmanagement engine 116 can modify operating parameters of medical devicesto help reduce alarm fatigue at the sensor level. In this aspect, eachuser fatigue model 104-108 is associated with a corresponding user, andalert management engine 116 is configured to perform the followingsteps. It receives user status data that includes at least one statusattribute. It receives patient status data from one or more medicaldevices where the medical devices have at least one an alarm conditionparameter. It updates the first user's user fatigue model based on thefirst user's status data. It determines an alarm fatigue level based onthe updated first user fatigue model. It modifies the at least oneoperational parameter of the at least one medical device based on atleast the alarm fatigue level of the user.

Turning to FIG. 9, FIG. 9 is a simplified flow diagram illustratingexample operations 200 that may be associated with alarm managementengine 116 according to some embodiments of system 100. In step 202,alarm management engine 116 receives status data. Status data collectedin step 202 can take many different forms, including physiological data,user shift data, and environmental alarm data. In step 204, a userfatigue model is updated based on the status data received in step 202.Multiple user fatigue models can be updated simultaneously or insequence as the data arrives—the system is limited in this respect bythe number of users accounted for in the system. Additionally, incomingstatus data can either overwrite previously stored status data within auser fatigue model or it can be stored alongside all previously storedstatus data to create a historical set of status data. In step 206, analarm fatigue level is determined based on the previously updated userfatigue model of step 204. In step 208, system 100 receives anindication of a medical alert requiring a response from a user. Thesystem can receive inputs from medical sensing equipment that collectphysiological information from a patient. When, for example, a medicalsensor indicates a heart rate has flat lined, an alert condition is met.In step 210, system 100 generates an alarm directed to a user takinginto account that user's alarm fatigue level. In step 212, system 100distributes the generated alarm based on the alarm fatigue level of step206 and the indication of an alert of step 208.

Turning to FIG. 10, FIG. 10 is a simplified diagram illustrating exampledetails and operations that may be associated with embodiments of system100. An alarm management engine 300 performs step 306 by determiningalarm fatigue based on status data collected from one or more medicaldevices 302 and performs step 308 by adjusting alarm conditions of oneor more medical devices 302 based on the one or more user fatiguelevels. In some embodiments, instead of simply altering alarms based onuser fatigue levels, alarm management engine 300 is configured to alteralarm conditions of one or more medical devices 302. An advantage ofaltering alarm conditions of a medical device is that each individualdevice 310, 312, 314, 316 can be modified to indicate an alarm based onone or more nonstandard operational parameters. For example, if aparticular medical device is prone to issuing excessive alarms because athreshold for meeting an alarm condition is too low (e.g., when a heartrate drops below 50 BPM, an alarm condition is met), the thresholdcomprising the alarm condition can be altered (e.g., such that the alarmcondition is met only when the heart rate drops below 30 BPM), thusreducing a number of alarms generated by that particular medical device.

In some embodiments, the alarm condition can comprise a threshold valuefor the patient status data, for example, above which the medical devicegenerates an alarm. For example, if the medical device is a heart ratemonitor and it is originally configured to generate an alarm if apatient's heart rate drops below 50 BPM, then system 100 can modify themedical device to generate the alarm only when that patient's heart ratedrops below 30 BPM.

In some embodiments, the alarm condition can comprise a maximumfrequency at which the medical device can generate an alarm (orindication of an alert). For medical devices that detect physiologicalinformation that changes very slowly, it can be advantageous to permitthe medical device to only generate an alarm every so often. Forexample, if a patient's body temperature drops below a threshold wherethe medical device will generate an alarm, the slow changing nature ofbody temperature may result in fluctuation about that threshold level.These fluctuations can in turn cause the medical device to generate manyalarms very quickly as the patient's body temperature changes rightaround the threshold temperature. System 100 can remedy this by alteringthe alarm condition of the medical device such that it can generate analarm only once every 10 minutes, which would allow the patient's bodytemperature to change without generating unnecessary alarms.

In some embodiments, the alarm condition can comprise a samplingfrequency of a medical device. Thus, changing the alarm condition canchange how frequently the medical device gathers physiologicalinformation from a patient. This can be advantageous in that it canreduce the number of alarms the medical device generates.

In some embodiments, the alarm condition can comprise measurementsensitivity, which can determine a minimum amount of change aphysiological measurement undergoes before the medical device willdetect that change. This can also help to reduce unnecessary alarmgeneration. In an example, if a medical device is set to detect bodytemperature changes only when the temperature change is 0.2 degrees orgreater, fewer alarms would be generated based on small fluctuationsaround the threshold value.

The same concepts can be extended to any type of medical device. Medicaldevices can include devices that directly measure physiologicalinformation, or machines that conglomerate that type of information(e.g., Phillips lntelliVue Patient Monitor and other comparable systemsknown in the art). Physiological quantities that can be measured bymedical devices include: monitoring for cardiac problems such asarrhythmias (e.g., atrial, fibrillation, ventricular fibrillation,ventricular tachycardia, and premature ventricular contractions) andasystole (i.e., lack of measurable electrical cardiac activity);monitoring for pulse rate problems such as tachycardia and bradycardia;monitoring for respiration problems (for example, via oximetry) such asapnea, tachypnea, and desaturation; and monitoring for blood pressureproblems such as hypertension and hypotension. Alarm conditions cancorrespond to a particular ECG waveform, or to an elevated blood toxinlevel in some embodiments. In some embodiments, alarms can include thephysiological measurements thereby conveying information to therecipient while simultaneously alerting the recipient that a response isrequired.

Several medical devices may operate concurrently in a Medical IntensiveCare Unit (MICU) and Coronary Care Unit (CCU). For example, a ventilatorand Swan-Ganz catheter (a flexible tube inserted through the right sideof the heart that monitors pressures and outflow from the heart) canboth generate alarms when the devices measure status data that meetsrespective internal alarm conditions. Any medical device connected toalarm management engine 300 can be modified suitably to change the alarmcondition within the medical device itself. The result is a reducednumber of alarms in the course of normal operation of system 100,thereby decreasing overall alarm fatigue of one or more system users.

Alternatively, alarms can be distributed over a centralized system, forexample, via alarm devices that are fixed in place throughout ahospital. The alarm devices can be capable of projecting lights ofdifferent colors and intensities, as well as sounds of differentfrequencies and intensities. One possible alarm device comprises amonitor such as a television with accompanying speakers where adistributed alarm can be presented as text, light, and/or sound. In someembodiments, system 100 can operate using an existing alarm system.System 100 can use the alarm distribution capabilities of the existingsystem to distribute its alarms. Regardless of the distribution method,in one way or another distribution of alarms will likely affect usersbeyond merely the target user. However, since the system is continually(or continuously) collecting status data from a plurality of users,these effects are taken into account in terms of each user's fatiguelevel.

Some embodiments of alarm management system 100 can operate inreal-time, monitoring relevant user activities as they occur and usingthe monitoring information to adjust its output. To do so, system 100employs a feedback loop. Status data updates a profile, which is used todetermine alarm fatigue level to generate an output, which in turninfluences the alarm fatigue level. For example, as a user experiencesmore and more alarms throughout a shift, that user's alarm fatiguerises. System 100, sensing and quantifying a rise in alarm fatiguelevel, modulates its alarm outputs to that user to mitigate the effectsof that user's alarm fatigue.

Thus, a closed-loop control system (e.g., aproportional-integral-derivative control system) can be implemented tomanage changes in alarm outputs. Closed-loop control systems can beunder-damped, over-damped, or critically damped. System 100 can beconfigured to account for any hysteresis of its feedback loop, wherehysteresis indicates a dependence of the output of system 100 on itscurrent input and its history of past inputs. In other words, system 100may compensate for possible staleness of data when contemplating howquickly that data can be used to influence output. To this end, system100 can maintain frames of reference in its database for alarm fatiguelevels that can span hours, days, weeks, months, and/or years.

One should also appreciate that system 100 is also able to monitortrends in available data in order to trigger shifts in how alarms are tobe presented. The trends could be with respect to alarm data, userbehavior data, or other types of data. System 100 can, in someembodiments, represent the trends as derivatives or rates of change ofone value versus a second value; dx/dy for example. It should beappreciated that the derivatives or rates can be empirically measuredacross appropriate corresponding units of measure. For example, the rateof change could be missed alarms per day where number of alarms missedcould be measured as a difference from one day to the next. Moreinteresting examples might include a derivative associated with othervalues besides time. As an example, consider a scenario where theseverity of an alarm (S) might correlated with number of missed alarms(M) for a specific individual. In such a scenario, dS/dM could beindicative of how well a user deals with alarm severity and that theuser's a fatigue model should include triggers for such measures.

Beyond merely measuring trends as first order derivatives, it should beappreciated the trends could also include higher order derivatives ofthe available, measurable metrics. As system 100 collects data overtime, system 100 can monitor the higher order derivatives to determineif one or more users are becoming fatigue at a faster rate for example.Returning to the example of missed alarms per unit time (dM/dt), system100 can be configured to accept a constant rate at which the alarms aremissed, at least to with a threshold. Note that when dM/dt is constant,its higher derivatives are zero; d2M/dt 2=0. However, should the higherorder derivatives shift to unacceptable non-zero values (e.g., muchgreater than zero), then the system can begin altering its alarmstrategy to address the possible indication that the user has becomefatigued. The reader is reminded that the technique can also beleveraged for higher order rates of changes that do not involve time;d2x/dy2, d 3x/dy3, d 4x/dy4, etc.

In some embodiments, system 100 can generate recommendations as to whatactions a user can/should take to reduce alarm fatigue levels (e.g.,take a day off or generate a new work schedule). However, there is adifference between what system 100 would recommend versus what might bepractical. For example, if a floor is short staffed, system 100 may beconfigured to consider a certain alarm fatigue level that is differentfrom the alarm fatigue level when the floor is not short-staffed. Insome embodiments, system 100 can recommend adjustments to shiftschedules. In other embodiments, system 100 can be configured tooverride its normal functions in the event of an emergency (e.g., anall-hands-on-deck emergency where maximum alert intensity is needed toget the attention of all relevant personnel).

To the extent that it can influence an ecosystem, system 100 may likelyhave to compensate for a certain level of fatigue and/or risk. In otherwords, external factors influencing alarm fatigue levels may be presentthat are unaccounted in system 100. While system 100 can monitor itsusers to minimize those impacts, risk can likely never be reduced tozero and so system 100 can take that risk into account when determiningalarm conditions.

In some embodiments, alarm management engine 300 additionally: (1)determines an alarm fatigue level delta corresponding to a differencebetween at least two consecutive alarm fatigue levels determined duringa user's shift; (2) analyzes the status data associated with each userfatigue model update used for each of the at least two consecutive alarmfatigue levels determined during the at least one historical user'sshift to determine at least one principal contributor to the at leastone alarm fatigue level delta (e.g., via, for example, principalcomponent analysis or eigenvalue decomposition it determines the causesof alarm fatigue by isolating the largest contributing factors); (3)receives an indication of a medical alert requiring a response from thefirst user; and (4) generates an alarm targeting the first user based onthe alarm fatigue level, the at least one principal contributor, and theindication of an alert (i.e., to try to prevent alarm fatigue before ithappens by addressing a particular person's own largest causes).

System 100 can exist within various operating system environments, suchas cOS™. The cOS™ clinical operating system is designed to bringtogether clinical, financial, operational and environmental data toidentify and solve complex healthcare problems such as quality, cost,and outcomes at a health system level, hospital level, service linelevel, physician level and the patient level. The cOS architecture isdesigned using principles of big data, enterprise data warehouse, andbig memory that allow for proactive decision support as opposed toretrospective business intelligence. Proactive decision support allowshealth system executives to predict and solve problems before they occur(e.g., solve alarm fatigue problems proactively rather thanretroactively). The cOS™ platform allows innovation to thrive because itis a completely open system with documented web services that can beleveraged by the health system as well as third party softwaredevelopment companies to build new and innovative applications.

Within the cOS™ system, system 100 can be implemented and can execute atvarious levels. For example, at a hospital level, system 100 can managealarm fatigue levels of hospital personnel and staff. At a physicianlevel, system 100 can be directed towards medical doctors currentlyworking a shift. At a patient level, system 100 can be implemented tomanage alarm fatigue levels of individuals working with a particularpatient.

Some embodiments of system 100 are capable of reporting performancestatistics (e.g., for JACHO) to verify that alarm fatigue levels havebeen reduced and that environments where system 100 has been deployedare safer than national average (e.g., system 100 has resulted inreduced overall alarm fatigue levels compared to the national averageresulting in better patient care).

In various embodiments, system 100 can be applied to any type ofscenario that requires attention of a user. One such application caninclude notifications within a computer operating system. The systemcould monitor user responses and determine when an alarm should bealtered to better capture the attention of a user experiencing alarmfatigue. For example, scheduling software can provide notifications toremind a user of important meetings, but each of those notifications isidentical and when a user has a very busy schedule that user may becomedesensitized to the alerts. The alarm management system can beadvantageously applied to such a system to help the user from missingimportant meetings or deadlines.

In addition to offices and hospitals, system 100 can also be implementedin fire stations, police stations, and/or military bases, industrialsettings, gaming environments, or in other industries where alarms andalerts are used heavily. For example, in a fire station, fire fightersmay be subjected to many alarms per day or per shift, which could leadto alarm fatigue if nothing is done to prevent the condition fromdeveloping. The same holds true for police stations. A consequence ofalarm fatigue in these situations may be delayed response times to anemergency such as a fire or a burglary. On military bases, soldiers areexposed to many different alarms that provide different types ofinformation. Developing alarm fatigue in this setting can lead to missedalarms, and in a scenario where, for example, an alarm is sounded in awar-zone, missing that alarm can mean the difference between life anddeath. In industrial settings, equipment and other alarms blaring overthe course of a day can result in decreased concentration of workers ontheir respective tasks at hand. In the operation of heavy machinery,performing tasks requiring high levels of precision, and/or handling ofdangerous materials, a lapse in concentration can lead to an accidentcausing injuries, deaths, damage to equipment, and loss of progress. Ingaming settings, an ability to perform at a high level and an immersionin the game can both be critical to a player's enjoyment of the game,which can be disrupted by an excess of warnings and/or alarms.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts disclosed herein. Moreover, ininterpreting both the specification and the claims, all terms should beinterpreted in the broadest possible manner consistent with the context.In particular, the terms “comprises” and “comprising” should beinterpreted as referring to elements, components, or steps in anon-exclusive manner, indicating that the referenced elements,components, or steps may be present, or utilized, or combined with otherelements, components, or steps that are not expressly referenced. Wherethe specification claims refers to at least one of something selectedfrom the group consisting of A, B, C . . . and N, the text should beinterpreted as requiring only one element from the group, not A plus N,or B plus N, etc.

The foregoing discussion provides many example embodiments of systemsand methods for alarm fatigue management. Although each embodimentrepresents a single combination of various elements, all possiblecombinations of the disclosed elements are intended to be included inthe broad scope of the disclosure. Thus if one embodiment compriseselements A, B, and C, and a second embodiment comprises elements Band D,then the scope of the disclosure is considered to include otherremaining combinations of A, B, C, or D, even if not explicitlydisclosed.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints andopen-ended ranges should be interpreted to include only commerciallypractical values. Similarly, all lists of values should be considered asinclusive of intermediate values unless the context indicates thecontrary. Note that any recitation of ranges of values herein is merelyintended to serve as a shorthand method of referring individually toeach separate value falling within the range. Unless otherwise indicatedherein, each individual value is incorporated into the specification asif it were individually recited herein.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember can be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. One ormore members of a group can be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is herein deemed to contain the groupas modified thus fulfilling the written description of all Markushgroups used in the appended claims.

Note that in this Specification, references to various features (e.g.,elements, structures, modules, components, steps, operations,characteristics, etc.) included in “one embodiment”, “exampleembodiment”, “an embodiment”, “another embodiment”, “some embodiments”,“various embodiments”, “other embodiments”, “alternative embodiment”,and the like are intended to mean that any such features are included inone or more embodiments of the present disclosure, but may or may notnecessarily be combined in the same embodiments. The use of any and allexamples, or exemplary language (e.g. “such as”) provided with respectto certain embodiments herein is intended merely to better illuminatethe invention and does not pose a limitation on the scope of theembodiments otherwise claimed. No language in the specification shouldbe construed as indicating any non-claimed essential.

In example implementations, at least some portions of the activitiesoutlined herein may be implemented in software in, for example, alarmmanagement engine 12. In some embodiments, one or more of these featuresmay be implemented in hardware, provided external to these elements, orconsolidated in any appropriate manner to achieve the intendedfunctionality. The various network elements may include software (orreciprocating software) that can coordinate in order to achieve theoperations as outlined herein. In still other embodiments, theseelements may include any suitable algorithms, hardware, software,components, modules, interfaces, or objects that facilitate theoperations thereof.

Furthermore, alarm management engine 12 and various other componentsdescribed and shown herein (and/or its associated structures) may alsoinclude suitable interfaces for receiving, transmitting, and/orotherwise communicating data or information in a network environment.Additionally, some of the processors and memory elements associated withthe various nodes may be removed, or otherwise consolidated such that asingle processor and a single memory element are responsible for certainactivities. In a general sense, the arrangements depicted in the FIGURESmay be more logical in their representations, whereas a physicalarchitecture may include various permutations, combinations, and/orhybrids of these elements. It is imperative to note that countlesspossible design configurations can be used to achieve the operationalobjectives outlined here. Accordingly, the associated infrastructure hasa myriad of substitute arrangements, design choices, devicepossibilities, hardware configurations, software implementations,equipment options, etc. Moreover, all methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

In some of example embodiments, one or more memory elements (e.g.,memory element 38, 68) can store data used for the operations describedherein. This includes the memory element being able to storeinstructions (e.g., software, logic, code, etc.) in non-transitory mediasuch that the instructions are executed to carry out the activitiesdescribed in this Specification. These devices may further keepinformation in any suitable type of non-transitory storage medium (e.g.,random access memory (RAM), read only memory (ROM), field programmablegate array (FPGA), erasable programmable read only memory (EPROM),EEPROM, etc., software, hardware, or in any other suitable component,device, element, or object where appropriate and based on particularneeds.

A processor can execute any type of instructions associated with thedata to achieve the operations detailed herein in this Specification. Inone example, processors (e.g., processor 36, 66) could transform anelement or an article (e.g., data) from one state or thing to anotherstate or thing. In another example, the activities outlined herein maybe implemented with fixed logic or programmable logic (e.g.,software/computer instructions executed by a processor) and the elementsidentified herein could be some type of a programmable processor,programmable digital logic (e.g., a field programmable gate array(FPGA), an erasable programmable read only memory (EPROM), anelectrically erasable programmable read only memory (EEPROM)), an ASICthat includes digital logic, software, code, electronic instructions,flash memory, optical disks, CD-ROMs, DVD ROMs, magnetic or opticalcards, other types of machine-readable mediums suitable for storingelectronic instructions, or any suitable combination thereof.

The information being tracked, sent, received, or stored in system 10could be provided in any database, register, table, cache, queue,control list, or storage structure, based on particular needs andimplementations, all of which could be referenced in any suitabletimeframe. Any of the memory items discussed herein should be construedas being encompassed within the broad term ‘memory element.’ Similarly,any of the potential processing elements, modules, and machinesdescribed in this Specification should be construed as being encompassedwithin the broad term ‘processor.’

It is also important to note that the operations and steps describedwith reference to the preceding FIGURES illustrate only some of thepossible scenarios that may be executed by, or within, the system. Someof these operations may be deleted or removed where appropriate, orthese steps may be modified or changed considerably without departingfrom the scope of the discussed concepts. In addition, the timing ofthese operations may be altered considerably and still achieve theresults taught in this disclosure. The preceding operational flows havebeen offered for purposes of example and discussion. Substantialflexibility is provided by the system in that any suitable arrangements,chronologies, configurations, and timing mechanisms may be providedwithout departing from the teachings of the discussed concepts.

Note also that the disclosed subject matter herein enables constructionor configuration of a medical device to operate on digital data (e.g.,raw sensor data, alarm condition, etc.), beyond the capabilities of ahuman or un-configured (e.g., off-the-shelf) medical device. Althoughthe digital data represents alarm conditions, it should be appreciatedthat the digital data is a representation of one or more digital modelsof alarm conditions and not the actual alarms themselves, which compriseactivities or operations performed by the medical device and/oradapters. By instantiation of such digital models in the memory of themedical device (and/or adapter), the medical device (and/or adapter) isable to manage the digital models in a manner that could provide utilityto an individual (e.g., a user of the system) that the individual wouldlack without such a tool.

It should also be noted that any language directed to a computer shouldbe read to include any suitable combination of computing devices,including servers, interfaces, systems, databases, agents, peers,engines, controllers, or other types of computing devices operatingindividually or collectively. One should appreciate the computingdevices comprise a processor configured to execute software instructionsstored on a tangible, non-transitory computer readable storage medium(e.g., hard drive, solid state drive, random access memory (RAM), flashmemory, read-only memory (ROM), etc.). The software instructions canconfigure a suitable computing device to provide the roles,responsibilities, or other functionality as discussed herein withrespect to the disclosed apparatus. In some embodiments, the variousservers, systems, databases, or interfaces exchange data usingstandardized protocols or algorithms, possibly based on hyper-texttransfer protocol (HTTP), hyper-text transfer protocol secure (HTTPS),Advanced Encryption Standard (AES), public-private key exchanges, webservice application programming interfaces (AP's), known financialtransaction protocols, or other electronic information exchangingmethods. Data exchanges preferably are conducted over a packet-switchednetwork, the Internet, local area network (LAN), wide area network(WAN), virtual private network (VPN), or other type of packet switchednetwork.

As used in the description herein and throughout the claims that follow,when a system, engine, server, device, module, or other computingelement is described as configured to perform or execute functions ondata in a memory, the meaning of “configured to” or “programmed to”refers to one or more processors or cores of the computing element beingprogrammed by a set of software instructions stored in the memory of thecomputing element to execute the set of functions on target data or dataobjects stored in the memory.

One should appreciate that the disclosed techniques provide manyadvantageous technical effects including reduction in latency between acomputing device ingesting healthcare data and generating a predictionor recommendation. Latency is reduced through storage of health caredata in a memory and in the form of N-grams, which can becomputationally analyzed quickly.

Although the present disclosure has been described in detail withreference to particular arrangements and configurations, these exampleconfigurations and arrangements may be changed significantly withoutdeparting from the scope of the present disclosure. For example,although the present disclosure has been described with reference toparticular communication exchanges involving certain network access andprotocols, system 10 may be applicable to other exchanges or routingprotocols. Moreover, although system 10 has been illustrated withreference to particular elements and operations that facilitate thecommunication process, these elements, and operations may be replaced byany suitable architecture or process that achieves the intendedfunctionality of system 10.

Numerous other changes, substitutions, variations, alterations, andmodifications may be ascertained to one skilled in the art and it isintended that the present disclosure encompass all such changes,substitutions, variations, alterations, and modifications as fallingwithin the scope of the appended claims. In order to assist the UnitedStates Patent and Trademark Office (USPTO) and, additionally, anyreaders of any patent issued on this application in interpreting theclaims appended hereto, Applicant wishes to note that the Applicant: (a)does not intend any of the appended claims to invoke paragraph six (6)or (f) of 35 U.S.C. section 112 as it exists on the date of the filinghereof unless the words “means for” or “step for” are specifically usedin the particular claims; and (b) does not intend, by any statement inthe specification, to limit this disclosure in any way that is nototherwise reflected in the appended claims.

1.-20. (canceled)
 21. A system for controlling an alarm, the systemcomprising: a medical device capable of generating an alarm; and acomputing device communicatively coupled to the medical device, whereinthe computing device comprises one more hardware processors configuredto: receive measurable data of a person responding to the alarm;calculate an alarm fatigue level of the person from the measurable data;and calculate an alarm threshold based on the calculated alarm fatiguelevel; wherein the medical device is configured to execute the alarmaccording to the calculated alarm threshold.
 22. The system of claim 21,wherein the measurable data is indicative of a likelihood of the personresponding to the alarm, wherein the measurable data comprisesphysiological information related to the person, wherein thephysiological information comprises one or more items selected from thegroup consisting of stress level, neurological activity, brainelectrical activity, hormone levels, body temperature, breathing rate,blood sugar level, age, change of position, and sleep activity.
 23. Thesystem of claim 21, further comprising a fatigue model databasecommunicatively coupled to the computing device and comprising at leastone fatigue model attribute, wherein the measurable data modifies the atleast one fatigue model attribute.
 24. The system of claim 23, furthercomprising a subscriptions database communicatively coupled to thecomputing device and comprising a subscription level for the medicaldevice, wherein the calculated alarm threshold is based on thesubscription level of the medical device.
 25. The system of claim 23,further comprising a clinical operating system (cOS) executing in aremote network with the fatigue model database, wherein the cOSfacilitates communication between the computing device and the fatiguemodel database.
 26. The system of claim 21, wherein the medical devicecomprises: a memory element for storing data; and a processor thatexecutes instructions associated with the data, wherein the processorand the memory element cooperate such that the medical device isconfigured for: receiving the calculated alarm threshold from thecomputing device; executing the alarm according to the alarm threshold;and generating an alarm indicator based on the executed alarm.
 27. Thesystem of claim 26, wherein the memory element and the processor of themedical device are included in an adapter that is detachably attached tothe medical device.
 28. The system of claim 26, wherein the memoryelement and the processor of the medical device are included in anadapter that is integrated into the medical device.
 29. The system ofclaim 26, wherein the executing comprises deleting the alarm based onthe calculated alarm threshold.
 30. The system of claim 26, wherein thealarm indicator comprises a feedback to the computing device, whereinthe calculated alarm threshold is further based on the feedback.
 31. Thesystem of claim 21, wherein the alarm is based on a physiologicalmeasurement of a patient by the medical device.
 32. The system of claim21, wherein the medical device determines a format of the alarmaccording to the calculated alarm threshold.
 33. The system of claim 21,wherein the medical device determines a distribution mode of the alarmaccording to the calculated alarm threshold.
 34. The system of claim 21,wherein the measurable data further comprises one or more items selectedfrom the group consisting of alarm response time information, shiftdata, and environmental alarm data that is indicative of a likelihood ofthe person responding to the alarm or another alarm.
 35. The system ofclaim 21, further comprising a network over which the medical devicecommunicates with the computing device.
 36. The system of claim 21,wherein the one or more hardware processors are configured to transmitthe calculated alarm threshold to the medical device, wherein thecalculated alarm threshold is configured to increase a likelihood of theperson responding to the alarm.
 37. A method of controlling an alarm,the method comprising: receiving measurable data of a person respondingto an alarm of a medical device; calculating an alarm fatigue level ofthe person from the measurable data; calculating an alarm thresholdbased on the calculated alarm fatigue level; and executing the alarmaccording to the executed alarm threshold.
 38. The method of claim 38,further comprising generating an alarm indicator based on the executedalarm.
 39. The method of claim 39, wherein the alarm is based on aphysiological measurement of a patient by the medical device.
 40. Themethod of claim 38, wherein the calculated alarm threshold is configuredto increase a likelihood of the person responding to the alarm oranother alarm.